Nowadays, when one can easily be swamped with data, the relevant question in many applications is no longer how to acquire data, but how to extract the most relevant information from it. Machine learning is a research field dealing with this kind of information processing, and a new paradigm from that field that gained a lot of popularity is Reservoir Computing (RC). The present invention relates to information processing, such as physical information processing, e.g., optical information processing, using this interesting paradigm of reservoir computing. Reservoir computing can find application in the analog domain, e.g., for analog signal classification or for implementing nonlinear analog filters or controllers for which no closed form specification is available, but also in the digital domain, e.g., for learning boolean functions or automata, signal regeneration or header recognition.
Like many methods in this field, reservoir computing is partly inspired by how the human brain works, but essentially, it is a method to use dynamical systems for computation. In reservoir computing, a dynamical system, further referred to as the computing reservoir, is excited by the inputs to be processed and its output states are trained to follow a desired output, e.g., by linear regression, while keeping the computing reservoir itself untrained. This is in contrast to recurrent neural network systems, which are notoriously difficult to train. The computing reservoir itself can be seen as a nonlinear pre-processor which projects a time-variant input signal into a higher dimensional space where it becomes easier to classify, e.g., using a linear classifier. For this, the reservoir is preferably in the proper dynamical regime at the edge of instability, such that the system is dynamic enough without becoming instable. When feedback from the output to the reservoir is allowed, any conceivable digital or analog computation on time-varying inputs, e.g., in the idealized case without noise, can be carried out. Even without such feedback, any problem that requires fading memory, which forms a broad class of problems, can be solved under some general and mild conditions. Reservoir computing advantageously offers a system which is easy to use, combined with computational capabilities matching or exceeding other state-of-the-art machine learning techniques for a broad range of applications such as speech recognition, time series prediction, pattern classification and robotics. Due to the lenient requirements for the computing reservoir, implementations have been demonstrated in the art on diverse hardware platforms ranging from a basin of water to cellular neural networks and bacteria.
Software-based state-of-the-art implementations of reservoir computing have in the recent past demonstrated good performance for a variety of tasks. However, dedicated hardware implementations may offer substantial speed gains and power savings.
For example, a photonics-based hardware implementation of RC allows for fully exploiting the advantages offered by light, e.g., low power, high bandwidth and inherent parallelism, for computational purposes, especially when the input information is already encoded in the optical domain such as in many telecom applications or in image processing. Optical computing reservoirs, based on a fibre and a single dynamical node, are known in the art. Appeltant et al. disclosed such an approach in Nature Communications 2, article number 468. However, fibre-based approaches may have the disadvantages of being fairly bulky, being not stable enough to exploit information encoded in the phase component of the light, having a not very flexible interconnection topology and offering poor scalability.
In other optical reservoir computing approaches known in the art, on-chip solutions with optical amplifiers have been used, for example as disclosed by Vandoorne et al. in Optics Express 16(15), pp. 11182-11192. Here, it was shown that integrated optical chips with a network of coupled Semiconductor Optical Amplifiers (SOAs) can be used for reservoir computing. This offers the advantage of a small footprint and permits the use of coherent light, such that a performance improvement can be achieved over real-valued networks traditionally used in software implementations as well as over fibre-based approaches. However, the integrated optical chip disclosed by Vandoorne et al. may be not very power efficient, e.g., due to the need for optical amplifiers. Furthermore, it requires a difficult technology, e.g., complex manufacturing processes and relatively costly components. Furthermore, such approach may only offer a limited speed, e.g., fundamentally limited by the carrier lifetime.